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cs.CL2025

Transferring Linear Features Across Language Models With Model Stitching

Alan Chen, Jack Merullo, Alessandro Stolfo +1

In this work, we demonstrate that affine mappings between residual streams of language models is a cheap way to effectively transfer represented features between models. We apply t…

cs.CL2025

Probing for Arithmetic Errors in Language Models

Yucheng Sun, Alessandro Stolfo, Mrinmaya Sachan

We investigate whether internal activations in language models can be used to detect arithmetic errors. Starting with a controlled setting of 3-digit addition, we show that simple…

cs.CL2025

Improving Instruction-Following in Language Models through Activation Steering

Alessandro Stolfo, Vidhisha Balachandran, Safoora Yousefi +2

The ability to follow instructions is crucial for numerous real-world applications of language models. In pursuit of deeper insights and more powerful capabilities, we derive instr…

cs.CL2024

Do Language Models Exhibit the Same Cognitive Biases in Problem Solving as Human Learners?

Andreas Opedal, Alessandro Stolfo, Haruki Shirakami +5

There is increasing interest in employing large language models (LLMs) as cognitive models. For such purposes, it is central to understand which properties of human cognition are w…

cs.CL2024

Groundedness in Retrieval-augmented Long-form Generation: An Empirical Study

Alessandro Stolfo

We present an empirical study of groundedness in long-form question answering (LFQA) by retrieval-augmented large language models (LLMs). In particular, we evaluate whether every g…